Abstract:

Various computer-implemented methods are provided. One method for sorting
defects in a design pattern of a reticle includes searching for defects
of interest in inspection data using priority information associated with
individual defects in combination with one or more characteristics of a
region proximate the individual defects. The priority information
corresponds to modulation levels associated with the individual defects.
The inspection data is generated by comparing images of the reticle
generated for different values of a lithographic variable. The images
include at least one reference image and at least one modulated image. A
composite reference image can be generated from two or more reference
images. The method also includes assigning one or more identifiers to the
defects of interest. The identifier(s) may include, for example, a defect
classification and/or an indicator identifying if the defects of interest
are to be used for further processing.

Claims:

1. A computer-implemented method for detecting defects in a design pattern
of a reticle, comprising:acquiring images of the reticle for different
values of a lithographic variable, wherein the images comprise two or
more reference images obtained at nominal values and one or more
modulated images;generating a composite reference image from the two or
more reference images;comparing at least two of the images, wherein the
at least two of the images comprise the composite reference image;
anddetermining if a defect is present in the design pattern using results
of said comparing.

2. The method of claim 1, wherein if the defect is determined to be
present, the method further comprises assigning the defect to a group
based on one or more characteristics of a region proximate the defect.

3. The method of claim 2, wherein the one or more characteristics of the
region comprise one or more characteristics of the design pattern in the
region of at least one of the two or more reference images.

4. The method of claim 2, wherein the one or more characteristics of the
region comprise one or more characteristics of the region in the at least
two of the images used for said comparing.

5. The method of claim 2, wherein the one or more characteristics of the
region comprise one or more characteristics of the region extracted from
a GDS or aerial image.

6. The method of claim 2, wherein the one or more characteristics of the
region comprise one or more characteristics of the region determined from
a high resolution image.

7. The method of claim 2, further comprising analyzing the one or more
characteristics of the region of one or more defects in the group to
determine if the group is an irrelevant defect group.

8. The method of claim 2, further comprising analyzing one or more
characteristics of one or more defects in the group to determine if the
group indicates a failure in the design pattern.

9. The method of claim 1, wherein the images further comprise images of an
entire swath of dies printed on a wafer using the reticle, and wherein
the at least two of the images used for said comparing further comprise
the images of all of the dies in the entire swath.

10. The method of claim 9, wherein modulated dies in the entire swath are
printed using the same value of the lithographic variable, which is
different than the value of the lithographic variable at which reference
dies are printed in the entire swath.

11. The method of claim 9, wherein modulated dies in the entire swath are
printed using the different values of the lithographic variable, and
wherein reference dies in the entire swath are printed using an
additional different value of the lithographic value.

12. The method of claim 1, wherein a user with knowledge of a layout of
dies printed on a wafer selects which of the at least two of the images
used for said comparing.

13. The method of claim 1, wherein said acquiring comprises acquiring
images of the design pattern printed on a wafer using the reticle.

14. A computer-implemented method for detecting defects in a design
pattern of a reticle, comprising:acquiring images of an entire swath of
dies printed on a wafer using the reticle, wherein at least two of the
dies are printed at different values of a lithographic
variable;subsequent to said acquiring, comparing at least two of the
images; anddetermining if a defect is present in the design pattern using
results of said comparing.

15. The method of claim 14, wherein the dies comprise modulated dies and
at least one reference die, and wherein a number of the modulated dies in
the entire swath is greater than or equal to a number of the at least one
reference die in the entire swath.

16. The method of claim 14, wherein the dies comprise two or more
reference dies, wherein the method further comprises generating a
composite reference image from the images of the two or more reference
dies, and wherein one of the at least two of the images used for said
comparing comprises the composite reference image.

17. The method of claim 14, wherein the dies comprise modulated dies and
at least one reference die, and wherein the modulated dies are printed at
values of the lithographic variable that are the same for each of the
modulated dies but different than the value of the lithographic variable
at which the at least one reference die is printed.

18. The method of claim 14, wherein the dies comprise modulated dies and
at least one reference die, and wherein the modulated dies are printed at
values of the lithographic variable that are different for each of the
modulated dies and different than the value of the lithographic variable
at which the at least one reference die is printed.

19. A computer-implemented method for detecting and sorting defects in a
design pattern of a reticle, comprising:acquiring images of the reticle
for different values of a lithographic variable;comparing at least two of
the images;determining if pixel differences in the at least two of the
images follow a typical or atypical trend over the different values of
the lithographic variable.

21. The method of claim 19, wherein the images comprise images of
modulated dies printed at the different values of the lithographic
variable and images of reference dies printed using an additional
different value of the lithographic variable.

22. The method of claim 19, wherein said acquiring comprises acquiring
images of the design pattern printed on a wafer using the reticle.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001]This application is a divisional of U.S. patent application Ser. No.
11/005,658 entitled "Computer-Implemented Methods for Detecting and/or
Sorting Defects in a Design Pattern of a Reticle," filed Dec. 7, 2004,
now U.S. Pat. No. 7,729,529 issued Jun. 1, 2010, both of which are
incorporated by reference as if fully set forth herein.

BACKGROUND OF THE INVENTION

[0002]1. Field of the Invention

[0003]The present invention generally relates to computer-implemented
methods for detecting and/or sorting defects in a design pattern of a
reticle. Certain embodiments relate to a computer-implemented method that
includes generating a composite reference image from two or more
reference images and using the composite reference image for comparison
with other sample images for defect detection. Other embodiments include
sorting defects using priorities, defect attributes, defect appearance
and background information. Additional embodiments relate to assisting
the user in locating the relevant and unique defects based on background
appearance and other characteristics combined with wafer design data and
knowledge of process modulation.

[0004]2. Description of the Related Art

[0005]The following description and examples are not admitted to be prior
art by virtue of their inclusion in this section.

[0006]The rapid decrease in k1 (line-width=k1 (λ/NA)) in
lithographic manufacture of semiconductor devices has necessitated the
use of Resolution Enhancement Techniques (RET). These RET include, but
are not limited to, Optical Proximity Corrections (OPC), Phase Shift
Masks (PSM), and assist bar corrections. Although they are implemented in
semiconductor device designs to facilitate low k1 lithography, these
RET make reticles more difficult and consequently more expensive to
manufacture.

[0007]Semiconductor device design and reticle manufacturing quality are
verified by different procedures before the reticle enters a
semiconductor fabrication facility to begin production of integrated
circuits. The semiconductor device design is checked by software
simulation to verify that all features print correctly after lithography
in manufacturing. Such checking is commonly referred to as "Design Rule
Checking." The output of this design rule checking can produce a
potentially large set of critical points, sometimes referred to as "hot
spots" on the reticle layout. This set can be used to direct a
point-to-point inspector, such as at a Review SEM, but this can be highly
inefficient due to the number of critical points. The reticle is
inspected at the mask shop for reticle defects and measured to ensure
that the features are within specification. Marginal RET designs not
noted by simulation checks translate into electrical failures in wafer
fabrication, affect yield, and possibly remain unnoticed until wafer
fabrication is complete.

[0008]Traditional methods employed in the inspection of complex mask
patterns place tremendous demand on reticle inspection tools. One
technique for performing image qualification entails using focus exposure
matrix techniques. Performing an inspection of a conventional focus
exposure matrix introduces a complication in that every exposure field is
different. Die-to-die comparison is performed between adjacent local
exposure fields. Any pattern change that may occur at a defocus position
that is physically located farther than one exposure field from the
nominal exposure field will not, therefore, be detected as different
because the nominal exposure field is no longer factored in the
comparison. Moreover, current reticle inspection techniques cannot detect
the presence of an error in the design database. Prior art single die
reticle inspection entails implementation of a design simulation
technique in which a signal derived from an actual reticle is subtracted
from a simulated design reference.

[0009]What is needed, therefore, is an inspection technique that is
effective in locating pattern anomalies in a single die or a multi-die
reticle and detecting reticle design errors resulting from errors in the
design data base.

[0010]Methods have been invented to address the above-described needs.
These methods are often referred to as "Process Window Qualification"
Methods or "PWQ" Methods and are described in U.S. Patent Application
Publication No. US2004/0091142 to Peterson et al., which is incorporated
by reference as if fully set forth herein. Software packages that are
configured to perform methods such as those described by Peterson et al.
are commercially available from KLA-Tencor, San Jose, Calif. In general,
the methods can be used to find design elements of a reticle that will
fail in lithographic processing when used with lithographic variables
(e.g., focus, dose, etc.) that are within a normal process window for the
reticle.

[0011]PWQ methods are often performed using wafer inspection tools such as
any of the wafer inspection tools that are commercially available from
KLA-Tencor. In one example, a wafer is printed with columns of dies, each
containing the design pattern on the reticle, in an N-M-N pattern. The
"N" dies are those dies that are printed with a "nominal" lithographic
variable (which may also be commonly referred to as a "nominal
lithography parameter," a "nominal lithographic process parameter," or a
"nominal process condition"). The "M" dies are printed with a value of
the lithographic variable that is different than the nominal lithographic
variable. In other words, the M dies are printed with a modulated
lithographic variable. The nominal lithographic parameter may be the
value of the lithographic parameter known to represent the "best
condition" for exposure of a wafer with the reticle. Alternatively, the
nominal lithographic parameter may be assigned a different baseline value
of the lithographic parameter. The lithographic variable can be modulated
positively and negatively with respect to the nominal lithographic
variable in rows of dies printed on the wafer.

[0012]After exposure of the wafer with the reticle, the wafer is inspected
by comparing the modulated die to the two nominal dies on either side of
the modulated die. Adjacent dies are compared after both of the adjacent
dies have been imaged. Therefore, the comparison is performed
sequentially in the order in which the dies are imaged. Differences
between the adjacent dies can be stored as potential defects.

[0013]Positively modulated dies and negatively modulated dies may be
handled separately for purposes of analysis. In addition, the defects
that are detected in the modulated dies may be analyzed to determine the
priority or relevance of the defects. Furthermore, the user may be able
to review the defects to find the critical or important defects that were
detected.

[0014]Although the above-described PWQ methods have proved successful in
meeting the needs outlined above, these methods can also be improved. For
example, in the inspection process, the modulated dies are compared to
exactly two nominal or reference dies. Randomly occurring defects in
either or both of the reference dies may adversely affect the results if
they result in reducing the priority of defects in the modulated dies. In
addition, using a three die comparison (i.e., two reference dies for each
modulated die) results in the use of most of the wafer area for printing
the reference dies.

[0015]In the PWQ software used today, potential failure points in the
design pattern are identified by looking for repeating defects.
Unfortunately, by its very nature, the experiment can produce an
overwhelming number of unimportant repeating defects, particularly in the
dies that are highly modulated. Automatic defect classification (ADC) is
one way to reduce the number of candidate defects. However, the inline
ADC (iADC) method that is available for PWQ uses additional information
about the defect itself, and much of this information is irrelevant to
finding the most likely failure points. A newer version of the iADC
method as described in U.S. patent application Ser. No. 10/954,968 to
Huet et al., now U.S. Pat. No. 7,142,992 issued Nov. 28, 2006, which is
incorporated by reference as if fully set forth herein, provides the
capability of focusing on background features. However, in these methods,
a user selects background features from the complete set of available
features that are used to classify defects thereby creating an extra step
in the setup of the inspection. Additionally, in current methods for
reviewing defects, it is difficult to obtain multiple examples of
potentially interesting defects.

[0016]The PWQ methods may also be altered to use a stored "golden die"
image for comparison to the modulated images. A "golden die" image may be
generally defined as an image of design pattern information on a reticle
that is known in some manner to be free of defects. Therefore, by using a
golden die image, the number of nominal reference dies printed on the
wafer may be reduced, or even eliminated, thereby allowing more modulated
dies to be printed on the wafer. However, there are disadvantages to
using such a golden die image. For example, a detailed golden die image
can require hundreds of Gbytes of storage. On the other hand, the detail
of the golden die image may be reduced, but compromising on the detail of
the golden die image compromises the effectiveness of the inspection
method. Furthermore, a golden die image most likely is not formed under
the same processing conditions as the test die, particularly if the
golden die image is generated by simulation or if the golden die image
was obtained from a different wafer than the wafer on which the modulated
dies are printed. The differences in formation of the golden die and the
modulated dies may result in false defect detection during inspection of
the modulated dies. Moreover, reading the golden image from storage media
can be slower that reacquiring the golden image from an image computer or
another computer system.

[0017]Accordingly, it may be advantageous to develop computer-implemented
methods for detecting and/or sorting defects in a design pattern of a
reticle that allows accurate defect detection while using relatively few
nominal reference dies, increases the accuracy of the defect detection by
reducing the adverse effects of defects in the nominal reference dies on
the accuracy of the defect detection, allows rapid identification and
removal of unimportant repeating defects so that these defects do not
obscure the defects of interest, allows multiple examples of interesting
defects to be found relatively easily, allows classification of defects
in a substantially automated manner, or achieves one or more of the above
improvements without using a stored golden die image of the design
pattern on the reticle.

SUMMARY OF THE INVENTION

[0018]One embodiment relates to a computer-implemented method for sorting
defects in a design pattern of a reticle. The method includes searching
for defects of interest in inspection data using priority information and
defect attributes associated with individual defects in combination with
one or more characteristics of a region proximate the individual defects
and one or more characteristics of defects. The inspection data is
generated by comparing images of the reticle generated for different
values of a lithographic variable. The images include at least one
reference image and at least one modulated image. The method also
includes assigning one or more identifiers to the defects of interest.

[0019]In one embodiment, the priority information is derived from the
relationship between inspected defects and their corresponding modulation
levels. In another embodiment, defect attributes contain simple defect
information such as location, size, intensity magnitude and polarity as
well as inspection parameters. Defects are filtered by defect priorities
and attributes. The filtering criteria can be selected by user. In some
embodiments, the one or more characteristics of regions proximate the
defects and on the defects are computed from reference and defect images,
respectively.

[0020]In another embodiment, the method may include grouping the defects
of interest based on the one or more characteristics of the region
proximate the individual defects or the one or more characteristics of
the defects, or a combination thereof. The characteristics used in
grouping are selected by the user. In a different embodiment, the method
may include retrieving defects which are similar to given defects based
on defect appearance, attributes and one or more characteristics of
region proximate the defects. The retrieving criteria can be selected by
the user.

[0021]In one embodiment, the one or more identifiers may include a defect
classification. In another embodiment, the one or more identifiers may
include an indicator identifying if the defects of interest are to be
used for further processing. In one such embodiment, assigning the one or
more identifiers is performed automatically based on the priority
information and defect classification.

[0022]In an additional embodiment, the method may include comparing the
potential defects of interest to the results generated by design rule
checking performed on design pattern data of the reticle to determine if
the defects of interest correlate to design rule checking critical
points. In one such embodiment, the method may also include removing from
the inspection data the defects that do not correlate with the critical
points. In a similar manner, the method may include comparing the
potential defects of interest to the results generated by optical rule
checking (ORC) performed on design pattern data of the reticle. In
general, steps described herein involving the use of DRC results may
alternatively be performed using ORC results. Each of the embodiments of
the method described above may include any other step(s) described
herein.

[0023]Another embodiment of the invention relates to a
computer-implemented method for detecting defects in a design pattern of
a reticle. The method includes acquiring images of the reticle for
different values of a lithographic variable. The images include two or
more reference images obtained at nominal values and one or more
modulated images. The method also includes generating a composite
reference image from the two or more reference images. In addition, the
method includes comparing at least two of the images. The at least two of
the images include the composite reference image. In one embodiment, the
user, with knowledge of the wafer layout or dies printed on the wafer,
informs the system which images will be used for reference (e.g.,
composite or non-composite) and for comparison. In this manner, the user
may select the images that are used for comparison. The method further
includes determining if a defect is present in the design pattern of the
reticle using results of the comparison.

[0024]In some embodiments, the one or more characteristics of the region
may be selected by a user. In another embodiment, simulated images, as
from GDS or simulated aerial images, are used to determine the
characteristic(s) of the background, based on the location of the defect
in the reticle. The characteristic(s) of the region may be extracted from
such images using any technique known in the art. In addition,
experimentally generated aerial images may be used in a similar manner.
In a different embodiment, high resolution images of the reticle may be
used to determine characteristic(s) of the background region proximate
the defect, based on the location of the defect in the reticle. A high
resolution image of the reticle may be obtained using any appropriate
high resolution imaging system known in the art. For example, several
commercially available reticle inspection systems are configured to form
high resolution images of the reticle.

[0025]In addition or alternatively, the critical points may be regrouped
or filtered using the "Defects Like Me" function described herein to
reduce the population. In this manner, inspecting, measuring, and/or
reviewing critical points that are similar may be identified or
eliminated.

[0026]In addition, the critical points identified by the DRC may be
overlaid with the inspection data generated as described herein. The
inspection data may be data generated by imaging a wafer on which one or
more modulated dies and one or more reference dies are printed.
Alternatively, the inspection data may include aerial images of the
reticle design pattern generated by simulation or experimentation. In
this manner, the defects of interest found as described herein may be
compared to inspection data generated by design rule checking to
determine if the defects of interest correlate to design rule checking
defects. The design rule checking defects that do not correlate with the
defects of interest may then be removed from the design rule checking
inspection data. In a similar manner, the defects of interest may be
compared to data generated by optical rule checking to determine if the
defects of interest correlate to optical rule checking defects.

[0027]In a further embodiment, the images may include images of an entire
swath of dies printed on a wafer using the reticle. In this embodiment,
the at least two images used for the comparison may include images of all
of the dies in the entire swath. In another such embodiment, modulated
dies in the entire swath are printed using the same value of the
lithographic variable, which is different than the value of the
lithographic variable at which reference dies are printed in the entire
swath. In yet another such embodiment, modulated dies in the entire swath
are printed using the different values of the lithographic variable. In
this embodiment, reference dies in the entire swath are printed using an
additional different value of the lithographic variable.

[0028]In some embodiments, acquiring the images includes acquiring images
of the design pattern printed on a wafer using the reticle. In other
embodiments, the images may include aerial images. Each of the
embodiments of the method described above may include any other step(s)
described herein.

[0029]Another embodiment relates to a different computer-implemented
method for detecting and sorting defects in a design pattern of a
reticle. This method includes acquiring images of the reticle for
different values of a lithographic variable. The method also includes
comparing at least two of the images. In addition, the method includes
determining if individual pixels are different in the design pattern
using results of the comparison. The method also includes determining if
pixel differences in the at least two images follow a typical or atypical
trend over the different values of the lithographic variable.

[0030]If pixel differences are determined to be present, the method may
include assigning the location to a group based on comparison to a trend
in a plot of one or more characteristics of the images of the defect as a
function of the different values of the lithographic variable. For
example, an atypical trend may be identified as a potentially relevant
defect location. The images used in the method include, in some
embodiments, images of modulated dies printed at the different values of
the lithographic variable and images of reference dies printed using an
additional different value of the lithographic variable.

[0031]An additional embodiment relates to another computer-implemented for
detecting defects in a design pattern of a reticle. This method includes
acquiring images of an entire swath of dies printed on a wafer using the
reticle. At least two of the dies are printed at different values of a
lithographic variable. The method also includes, subsequent to the
acquisition of the images of the entire swath, comparing at least two of
the images. In addition, the method includes determining if a defect is
present in the design pattern using results of the comparison.

[0032]In one embodiment, the dies include modulated dies and at least one
reference die. In another embodiment, the dies may include two or more
reference dies as defined in the inspection recipe. In this embodiment,
the method may also include generating a composite reference image from
the images of the two or more reference dies. In such an embodiment, one
of the at least two of the images used for the comparison includes the
composite reference image. In an additional embodiment, the dies may
include modulated dies and at least one reference die. Each of the
embodiments of the method described above may include any other step(s)
described herein.

[0033]Further embodiments relate to a carrier medium that includes program
instructions executable on a computer system to perform any of the
computer-implemented methods described herein. Additional embodiments
relate to a system configured to perform any of the computer-implemented
methods described herein. The system may include a processor configured
to execute program instructions for performing one or more of the
computer-implemented methods described herein. In one embodiment, the
system may be a stand-alone system. In another embodiment, the system may
be a part of or coupled to an inspection system such as a wafer imaging
system or an aerial imaging measurement system. In a different
embodiment, the system may be a part of or coupled to a defect review
system. In yet another embodiment, the system may be coupled to a fab
database. The system may be coupled to an inspection system, a review
system, and/or a fab database by a transmission medium such as a wire, a
cable, a wireless transmission path, and/or a network. The transmission
medium may include "wired" and "wireless" portions.

BRIEF DESCRIPTION OF THE DRAWINGS

[0034]Further advantages of the present invention may become apparent to
those skilled in the art with the benefit of the following detailed
description of the preferred embodiments and upon reference to the
accompanying drawings in which:

[0035]FIGS. 1-4a are schematic diagrams illustrating plan views of
different configurations of dies printed on a wafer with a reticle for
different values of a lithographic variable;

[0036]FIG. 5 is a graph illustrating examples of different trends in plots
of a characteristic of images of defects as a function of different
values of a lithographic variable;

[0037]FIGS. 6-7 are screenshots illustrating examples of different user
interfaces that can be used to sort defects detected by the methods
described herein;

[0038]FIG. 8 is a schematic diagram illustrating a side view of one
embodiment of a system that can be used to perform one or more of the
computer-implemented methods described herein; and

[0039]FIG. 9 is schematic diagram illustrating a side view of an apparatus
that can be used to acquire aerial images of a design pattern of a
reticle.

[0040]While the invention is susceptible to various modifications and
alternative forms, specific embodiments thereof are shown by way of
example in the drawings and may herein be described in detail. The
drawings may not be to scale. It should be understood, however, that the
drawings and detailed description thereto are not intended to limit the
invention to the particular form disclosed, but on the contrary, the
intention is to cover all modifications, equivalents and alternatives
falling within the spirit and scope of the present invention as defined
by the appended claims.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0041]As used herein, the term "defect" refers to a defect in a design
pattern of a reticle that may cause a defect in a design pattern printed
on a wafer using the reticle such as excessive corner rounding,
unsatisfactory dimensions, missing features, bridging between features,
etc. In particular, the methods described herein are particularly
suitable for detecting defects in resolution enhancing technology (RET)
features of the design pattern.

[0042]The terms "reticle" and "mask" are used interchangeably herein. A
reticle generally includes a transparent substrate such as glass,
borosilicate glass, and fused silica having a layer of opaque material
formed thereon. The opaque regions may be replaced by regions etched into
the transparent substrate.

[0043]Many different types of reticles are known in the art, and the term
reticle as used herein is intended to encompass all types of reticles.
For example, the term reticle refers to different types of reticles
including, but not limited to, a clear-field reticle, a dark-field
reticle, a binary reticle, a phase-shift mask (PSM), an alternating PSM,
an attenuated or halftone PSM, and a ternary attenuated PSM. A
clear-field reticle has field or background areas that are transparent,
and a dark-field reticle has field or background areas that are opaque. A
binary reticle is a reticle having patterned areas that are either
transparent or opaque. Binary reticles are different from phase-shift
masks (PSM), one type of which may include films that only partially
transmit light, and these reticles may be commonly referred to as
halftone or embedded phase-shift reticles. If a phase-shifting material
is placed on alternating clear spaces of a reticle, the reticle is
referred to as an alternating PSM, an ALT PSM, or a Levenson PSM. One
type of phase-shifting material that is applied to arbitrary layout
patterns is referred to as an attenuated or halftone PSM, which may be
fabricated by replacing the opaque material with a partially transmissive
or "halftone" film. A ternary attenuated PSM is an attenuated PSM that
includes completely opaque features as well.

[0044]A reticle, as described herein, may or may not include a pellicle,
which is an optically transparent membrane that seals off the reticle
surface from airborne particulates and other forms of contamination. The
term reticle may also be used to refer to a reticle that includes optical
proximity correction (OPC) features. OPC features are designed to reduce
distortions of an image printed using the reticle by reducing optical
proximity effects. The term "optical proximity effects" generally refers
to variations in lateral dimensions or shapes of printed features due to
the proximity of other features on the reticle. Such effects may be
reduced by determining the distortions due to the optical proximity
effects and altering the features on the reticle to compensate for such
distortions.

[0045]RET such as OPC are increasingly being applied to integrated circuit
(IC) designs in order to print features on device wafers which are
smaller than the wavelength of light used as the exposure source. These
RETs often involve the addition of extra features to the design including
sub-resolution assist features (SRAF) and serifs with the result that the
layout of the design on the photomask or reticle becomes extremely
complex. Verifying that the RET features will print correctly on the
reticle and that the SRAFs will not print on the wafer but will cause the
main features to print correctly on the wafer is becoming an increasingly
difficult task. Furthermore, optical effects such as mask error
enhancement factor (MEEF) may cause additional distortion of the final
image at the wafer level. MEEF may be generally defined as the ratio of
the critical dimension of a feature printed in a resist to the critical
dimension of a structure formed on a reticle.

[0046]As used herein, the term "wafer" generally refers to substrates
formed of a semiconductor or non-semiconductor material. Examples of such
a semiconductor or non-semiconductor material include, but are not
limited to, monocrystalline silicon, gallium arsenide, and indium
phosphide. Such substrates may be commonly found and/or processed in
semiconductor fabrication facilities.

[0047]A wafer may include one or more layers formed upon a substrate. For
example, such layers may include, but are not limited to, a resist, a
dielectric material, and a conductive material. A "resist" may include
any material that may be patterned by an optical lithography technique,
an e-beam lithography technique, or an X-ray lithography technique.
Examples of a dielectric material may include, but are not limited to,
silicon dioxide, silicon nitride, silicon oxynitride, and titanium
nitride. Additional examples of a dielectric material include "low-k"
dielectric materials such as Black Diamond® which is commercially
available from Applied Materials, Inc., Santa Clara, Calif., and
CORAL® commercially available from Novellus Systems, Inc., San Jose,
Calif., "ultra-low k" dielectric materials such as "xerogels," and
"high-k" dielectric materials such as tantalum pentoxide. In addition,
examples of a conductive material include, but are not limited to,
aluminum, polysilicon, and copper.

[0048]One or more layers formed on a wafer may be patterned. For example,
a wafer may include a plurality of dies, each having repeatable pattern
features. Formation and processing of such layers of material may
ultimately result in completed semiconductor devices. As such, a wafer
may include a substrate on which not all layers of a complete
semiconductor device have been formed or a substrate on which all layers
of a complete semiconductor device have been formed. The term
"semiconductor device" is used interchangeably herein with the term "IC."
In addition, other devices such as microelectromechanical (MEMS) devices
and the like may also be formed on a wafer.

[0049]Turning now to the drawings, it is noted that the figures are not
drawn to scale. In particular, the scale of some of the elements of the
figures is greatly exaggerated to emphasize characteristics of the
elements. It is also noted that the figures are not drawn to the same
scale. Elements shown in more than one figure that may be similarly
configured have been indicated using the same reference numerals.

[0050]FIG. 1 illustrates one example of a configuration of dies printed on
wafer 10 with a reticle for different values of a lithographic variable.
In this example, reference or nominal dies N are printed on the wafer at
a reference value for the lithographic variable that is being evaluated.
The terms "reference die" and "nominal die" are used interchangeably
herein. The reference value may be the best known value for the
lithographic variable (e.g., best dose, best focus, etc.). Alternatively,
the reference value may be any predetermined baseline value.

[0051]The lithographic variable that is being evaluated may include any
lithographic parameter that may alter the design, pattern that is printed
on the wafer by the reticle. Examples of such lithographic variables
include, but are not limited to, dose, focus, partial coherence, and
numerical aperture. It may be particularly desirable to evaluate the
effect that different values of focus will have on the design pattern
since this is typically the lithographic parameter that will change most
often over time for a lithography process.

[0052]As also shown in FIG. 1, modulated dies M1, M2, and M3 are printed
on the wafer. The modulated dies are printed on the wafer at a value of
the lithographic variable that is different than the reference value at
which the nominal dies are printed. Although in the figure, the M1, M2
and M3 dies are in different rows, this is not a restriction of the
invention. The different values at which the modulated dies are printed
may vary depending upon, for example, the degree to which the
lithographic variable may be varied (e.g., the smallest increment change
in the lithographic variable that can be made on the lithography tool),
the typical process window for the lithographic variable, and/or the
number of modulated dies that can be printed on the wafer (e.g., in this
example, the number of rows of dies that can be printed on the wafer). In
one particular example, it may be desirable to evaluate how the design
pattern will be printed across the typical process window of the
lithographic variable for a lithography process. Therefore, the range of
the values of the lithographic variable to be evaluated may be divided by
the number of modulated dies that can be printed on the wafer to
determine appropriate increments in the different values of the
lithographic variable. However, appropriate values for the lithographic
variable may be determined in any other manner.

[0053]Although three rows of dies are shown printed on the wafer in FIG.
1, it is to be understood that the number of rows of dies that are
printed on the wafer will vary depending on, for example, the dimensions
of the dies and the dimensions of the wafer. In addition, although two
sets of dies (each set including an N-M-N sequence of dies) are shown in
FIG. 1 to make up each row of dies, it is to be understood that the
number of sets of dies in each row may also vary depending upon the
dimensions of the dies and the dimensions of the wafer.

[0054]To inspect the design pattern, the dies in a row are imaged in a
swath. The dies may be imaged using, for example, the wafer inspection
system described herein or any other appropriate tool in the art such as
wafer inspection systems that are commercially available from KLA-Tencor,
San Jose, Calif. The dies in a row may be imaged in the swath direction
shown in FIG. 1 or in the opposite direction.

[0055]After imaging two adjacent dies printed on the wafer, the images of
the two dies will be compared as shown by the arrows in FIG. 1. In
particular, the first nominal die in the swath is imaged and saved. After
imaging the adjacent M1 die, the images of the nominal die N and the
adjacent modulated die M1 are compared, and any differences between the
two dies are saved or otherwise noted, recorded, stored, etc. The
presence of defects in the dies may then be determined using the results
of the comparison. For example, to determine if the differences between
the two dies are defects, a threshold-type defect detection algorithm may
be applied to the difference data to determine if the differences are
indicative of defects.

[0056]The image of the M1 die may also be saved for comparison with the
other adjacent nominal die N after imaging of this nominal die in the
swath. Images of these two dies may then be compared as described above,
and defects may be detected based on the results of the comparison as
described above.

[0057]Since the modulated M1 die is compared with two reference dies, the
configuration shown in FIG. 1 allows double detection of defects in the
M1 die. In other words, if a randomly caused defect appears in the first
reference die, then the differences between the images of the first
reference die and the M1 die may indicate the presence of a defect in the
M1 die even though the defect is actually present in the first reference
die. However, the probability that the randomly caused defect will appear
in the same position in the second reference die is substantially low.
Therefore, when the image of the M1 die is compared to the second
reference die, the defect that was found in the first comparison will
most likely not be found in the second comparison. As such, defects that
are found in only one of the two comparisons may be labeled as false
defects and may be eliminated from any further evaluation.

[0058]Although the "double detection" of defects that is provided by
comparing each modulated die with two different nominal dies effectively
reduces the number of false defects that are detected, there are some
disadvantages to such methods. For example, a substantial amount of space
on the wafer is used for printing nominal dies thereby reducing the
number of modulated dies that can be printed on the wafer, which in turn
reduces the number of different values of the lithographic variable that
can be evaluated. Therefore, it would be advantageous to reduce the
number of reference dies that are printed on the wafer without reducing
the accuracy of the defect detection method.

[0059]Several improvements to the above-described defect detection method
are described below. It is important to note that each improvement may be
used alone or in combination with one or more of the other improvements.

[0060]One improvement can be realized by increasing the number of dies
that can be imaged and processed simultaneously. For example, as
described above, two dies are imaged (a nominal die and a modulated die),
the images of the two dies are compared to detect differences between the
images, and the differences are examined to identify those differences
that indicate defects. Therefore, only two dies are processed at one
time. In an alternative, three dies may be imaged (two nominal dies and
one modulated die), and these images may be processed simultaneously or
in "real time" to detect defects in the modulated die.

[0061]It would be advantageous, however, if more image data could be
processed simultaneously. For example, according to one embodiment, the
images that are acquired may include images of an entire swath of dies
printed on a wafer using the reticle. The images of the entire swath of
dies can then be examined by the inspection algorithm before flagging
defects. In other words, any useful or meaningful comparisons between any
of the dies in the entire swath may be made prior to analyzing the
differences between the images for defect detection. In addition, the die
layout in the swath will be known a priori. In this manner, the
computer-implemented method may select the appropriate die images for
comparison based on the position of the die images within the swath. In
another embodiment, a user with knowledge of a layout of dies printed on
a wafer can select which of the images are used for the comparison.

[0063]Being able to simultaneously process image data from an entire swath
of dies on a wafer provides several advantages. For example, image data
may be acquired for the entire swath including two or more reference dies
obtained at nominal values and one or more modulated dies. If two or more
reference dies are included in the swath, the methods described herein
may include generating a composite reference image from the two or more
reference images. For example, as shown in FIG. 2, a composite reference
image may be generated from all four of the reference dies included in a
swath. However, the composite reference image may be generated from fewer
than all of the reference dies in the swath. In addition, as shown in
FIG. 2, a composite reference image may be generated from the reference
dies in one swath on the wafer, and other composite reference images may
be generated for other swaths on the wafer. In this manner, a composite
reference image may be generated in real time after each swath is imaged.
However, a composite reference image may alternatively be generated from
two or more reference dies, and the same composite reference image may be
used for defect detection in modulated dies in the same or different
swaths on the wafer.

[0064]The composite reference image may be generated in any manner known
in the art (e.g., averaging the image data of the two or more reference
dies). In addition, it may be desirable to align the individual reference
die images prior to generating the composite reference image. In one
example, the reference die image frames in a swath may be aligned to a
common coordinate reference, and any misalignment in the frames may be
corrected by sub-pixel interpolation of pixel values. The modulated dies
may be aligned in a similar manner.

[0065]In any case, the composite reference image may be used for
comparison with the modulated dies in the swath as shown by the arrows in
FIG. 2. In other words, one of the at least two images used for
comparison may include the composite reference image. Using a composite
reference image for defect detection exploits the presence of multiple
nominal dies in a swath to improve the stability of the reference image
against which each of the modulated dies is compared. The use of a
composite reference image may also improve the sensitivity of the
detection by reducing the effects of random noise that may be present in
the individual reference die images. In other words, the methods
described herein are advantageous in that the signal-to-noise ratio of
the data used for defect detection may be increased, which may in turn
lead to the ability to isolate the most likely relevant defects.

[0066]Using the composite reference image for comparison with the
modulated die images may also advantageously allow the number of
reference dies included in the swath to be reduced. For example, as
described above, double defect detection is advantageous in that it
allows false defects caused by defects in the reference die instead of
the modulated die to be eliminated from the detection results thereby
increasing the accuracy of the defect detection methods. However, when
the composite reference image is generated from two or more reference
images, any differences between the reference images may be detected, and
these differences may be analyzed to determine if defects are present in
the reference images. Any defects that are determined to be present in
one or more of the reference images may then be removed from the image
data of the individual reference dies. The "scrubbed" image data may then
be used to generate the composite reference image.

[0067]It is important to note that in the methods described herein, if an
entire swath of die images can be generated and processed simultaneously,
the reference image that is used for comparison may be the composite
reference image as described herein or an individual reference image.
Even if two or more non-composite reference images are used for
comparison to modulated dies, the number of reference dies in the swath
can be reduced from the number currently being used in the N-M-N
configuration since the individual reference die images can be used and
reused for comparison to modulated die images regardless of the position
of the reference and modulated die images in the swath.

[0068]Using fewer nominal dies on a wafer advantageously allows more space
on the wafer to be used for modulated dies. Therefore, by using data more
efficiently and thoroughly, the methods described herein are able to
inspect more examples of dies that are modulated and fewer examples of
reference dies. One other inspection technique that can be used to reduce
the number of reference dies that are printed on a wafer is to compare
the modulated dies to a golden die image that is constructed from design
information or prior scanning and then stored on some medium such as a
database. Efficient data use as described herein, however, is potentially
a more cost effective, accurate, and faster method than using golden die
images from a database.

[0069]Unlike the configurations of the dies shown in FIGS. 1 and 2, when
fewer nominal dies can be used without reducing the accuracy of the
detection method, the number of modulated dies may be increased. One such
configuration is illustrated in FIG. 3. In this configuration, the number
of modulated dies in a swath is equal to the number of reference dies in
the swath. In addition, every other die position includes a different
type of die in an N-M-N-M configuration. However, the modulated and
nominal dies may be arranged in any other manner in the swath. For
example, the first two dies in the swath may be reference dies, and all
other dies in the swath may be modulated dies. In any case, reducing the
number of reference dies used by the method allows more modulated dies to
be printed on a wafer thereby allowing the design pattern of the reticle
to be examined for defects at more values of the lithographic variable
being altered.

[0070]As shown in FIG. 3, the reference die images in a swath may be used
to generate a composite reference image. The composite reference image
may be generated as described above. In addition, the composite reference
image may be used for comparison with the acquired images of the
modulated dies as described above. In addition, as shown in FIG. 3,
images of each of the reference dies in an entire swath may be used to
generate the composite reference image. Alternatively, images of fewer
than all of the reference dies in an entire swath may be used to generate
the composite reference image. Furthermore, as described above, a
composite reference image may be generated for each swath on the wafer
that is imaged. Alternatively, one composite reference image may be
generated and used for comparison to images of modulated dies in more
than one swath on the wafer.

[0071]As further shown in the configuration of FIG. 3, modulated dies in
the entire swath may be printed using the same value of the lithographic
variable. In particular, each of the modulated dies in one swath are M1
modulated, each of the modulated dies in another swath are M2 modulated,
etc. In other words, if the lithographic variable that is being evaluated
is focus, each of the M1 modulated dies may be printed at the same focus
value, each of the M2 modulated dies may be printed at a different focus
value that is the same for each M2 die, and so on. The value of the
lithographic variable used to print the dies in each swath is also
preferably different than the value of the lithographic variable used to
print the reference dies such that meaningful comparisons may be made
between the modulated dies and the reference dies.

[0072]Imaging an entire swath having the configuration shown in FIG. 3 and
performing defect detection as described above allows multiple similarly
modulated dies to be inspected at the same time. Performing defect
detection for more than one die modulated in a similar manner provides
more information about the design pattern and the defects detected in the
design pattern. For example, defects may be identified as randomly
occurring defects if the defect shows up in fewer than all of the
similarly modulated dies. Using the configuration shown in FIG. 3,
therefore, an entire swath may be imaged, and these images may be used to
detect defects in dies having the same value of the modulated
lithographic variable.

[0073]A different configuration is illustrated in FIG. 4 in which the
modulated dies in one swath have different values of the modulated
lithographic variable. In other words, the dies are laid out such that
the modulation varies along a row rather than in a column as shown in
FIGS. 1-3. In particular, the modulated dies in one swath may be M1
modulated, M2 modulated, M3 modulated, and so on. In this manner, if the
lithographic variable that is being evaluated is focus, the focus at
which the M1 die is printed may be 0.1 μM, the focus at which the M2
die is printed may be 0.2 μm, the focus at which the M3 die is printed
may be 0.3 μm, and so on. It is to be understood that these focus
values are merely intended to be examples of modulated focus values for
illustrative purposes and are not to be interpreted as limiting or
otherwise exemplary examples. The value of the lithographic variable used
to print the modulated dies in each swath is also preferably different
than the value of the lithographic variable that is used to print the
reference dies such that meaningful comparisons may be made between the
modulated dies and the reference dies.

[0074]Imaging an entire swath having the configuration shown in FIG. 4 and
performing defect detection as described above allows differently
modulated dies to be inspected at the same time. Therefore, this
configuration may be advantageously used to examine defects in the design
pattern across an entire range of values of the lithographic variable in
one swath. As such, one swath may be imaged and a substantial amount of
defect data may be generated from the imaged swath in a relatively short
amount of time. In addition, since the swath may include substantially
more modulated dies than was previously available, the modulated dies in
one swath may be printed at values of the lithographic variable spanning
an entire typical process window for the reticle. In this manner, one
swath may be imaged, and the swath image may be used to examine the
process window of the reticle in a substantially short amount of time,
particularly when compared to previously used process window
qualification methods.

[0075]The configuration shown in FIG. 4 may also be used to examine the
entire trend of any pixel property as a function of modulation at each
pixel location (x, y). A "trend" may be generally defined as how a
characteristic of images at a particular pixel location such as pixel
intensity varies as a function of different values of a lithographic
value. As such, trends at particular pixel locations may be expressed by
a plot such as those shown in FIG. 5. As shown in FIG. 5, a number of
trends that are relatively similar for a particular property at a pixel
location as a function of modulation may be defined as "typical trends."
Whether or not these "typical trends" are indicative of non-defective
pixel properties may be established in advance by another method (e.g.,
defect review). The "typical trends" may be established experimentally
through wafer or aerial image experiments or empirically through
simulations (e.g., aerial image simulation).

[0076]Trends that appear to be atypical may be flagged as potential
defects of interest or a potentially relevant defect location. In another
embodiment, defects may be detected by comparing at least two images
printed or acquired at different values of a lithographic variable. The
images may include images of modulated dies printed at different values
of a lithographic variable and images of reference dies printed using an
additional different value of the lithographic variable as described
herein. In some embodiments, if a defect is determined to be present, the
method may include assigning the defect to a group based on a trend in a
plot of one or more characteristics of the images of the defect as a
function of the different values of the lithographic variable.

[0077]The trend-based defect detection method described above is based on
the assumption that line width variations and line-end pull backs that
occur as a function of modulation will affect a larger number of pixels
and follow certain trends while the occasional "short" or other anomalous
events will occur in smaller numbers and follow a different trend.
Therefore, it is clear that different defect detection methods may be
used in the methods described herein to exploit information from multiple
modulated dies in a single swath. In addition, the trend-based defect
detection method described above may be performed for differently
modulated dies in a single swath or differently modulated dies in
different swaths. In other words, the trend-based defect detection method
may be used regardless of the die configuration on the wafer.
Furthermore, the trend-based defect detection method may advantageously
detect defects of interest (DOIs) while ignoring the large number of
unimportant image differences (such as line width variations, line-end
pull-backs, etc.) that will also occur as the lithographic variable is
modulated.

[0078]In another example of a trend-based defect detection method, a
point-to-point inspection on a relatively high resolution tool such as a
critical dimension scanning electron microscope (CD SEM) or a Review SEM
could be used to perform measurements and/or defect detection from the
nominal die outward in the modulated dies. In other words, a
point-to-point inspection based on the PWQ-type defect detection results
may be performed. This inspection of the PWQ-type defects may be
performed for the nominal dies and the modulated dies up to the point of
failure in the design pattern. The corresponding points in the nominal
and modulated dies that exhibit normal or expected variation or
degradation may be filtered out as non-defective or irrelevant. Any
remaining defects at these points may be classified (e.g., using
automatic defect classification (ADC)) to look for bridging or other
defect types that are relevant to process window errors. For example, for
measurements such as CD measurements, the method may include determining
if a "normal" variation in the CD measurements is present. This
determination may be made using a recipe based on the predominant feature
direction (the predominant trend in the feature characteristic being
measured). As such, relevant variations in the feature could be
distinguished from irrelevant variations in the feature. In a further
example, for defect detection, an ADC type inspection could be used to
search for classic kinds of failure in the design pattern such as
bridging features.

[0079]FIG. 4a illustrates another die configuration that can be used as
described herein. In this configuration, exposure dose, E, can be
modulated in column 2. PWQ type defects can be detected by comparison of
the dies in column 2 with a corresponding die in columns 0, 1, and/or 3.
The exposure dose can also be modulated in column 5. Defects can be
detected by comparing the dies in column 5 with the corresponding
reference dies in columns 3 and/or 4. In addition, exposure and dose
modulation may be examined on one wafer. For example, as shown in FIG.
4a, focus, F, may also be modulated in columns 7 and 10 on the wafer.
Defects may be detected in these modulated dies by comparison of the dies
with reference dies in the corresponding rows of columns 8, 9, and/or 11.
In this manner, modulation of exposure dose and focus may be examined
separately on one wafer. The die configuration shown in FIG. 4a may be
further configured as described herein.

[0080]Each of the die configurations described herein may be used by a
computer-implemented method to detect and/or sort defects in a design
pattern of a reticle. In particular, the die configurations described
herein may be used in PWQ-type defect detection methods. For example, as
described above, images of the reticle for different values of a
lithographic variable may be acquired. In particular, acquiring the
images may include acquiring images of the design pattern printed on a
wafer using the reticle. These images may be acquired using, for example,
the system described herein. In addition, at least two of the images may
be compared. The method also include determining if a defect is present
in the design pattern of the reticle using results of the comparison.

[0081]As noted above, however, a relatively large number of unimportant or
irrelevant image differences may be detected due to the very nature of
the methods described herein. The large number of unimportant differences
can result in the detection of a relatively large number of irrelevant
defects and false defects. The detection of irrelevant and/or false
defects in such large numbers may have several disadvantages. For
example, in order to identify the defects of interest, a user or a
software program would have to sort through all of the irrelevant and/or
false defects. Obviously, such sorting of the detected defects would
reduce the throughput of the process of finding defects of interest.

[0082]As further noted above, the trend-based defect detection method may
be used to differentiate between meaningful defects (or defects of
interest) and irrelevant defects. It may also be advantageous to quickly
and accurately differentiate between defects of interest and irrelevant
defects after the defect detection has been performed. In other words, it
may be desirable to perform defect classification to distinguish between
defects of interest and irrelevant defects. One problem with currently
used defect classification methods for use with the type of defect data
that is generated as described herein is that the defect classification
methods tend to focus on characteristics of the defects themselves to
identify the classification to which the defect belongs. In particular,
due to the modulation of the lithographic variable, the same defect may
appear differently in differently modulated dies. Therefore, one defect
may be assigned different classifications depending on the die in which
it is detected.

[0083]According to one embodiment, a more accurate and useful defect
classification method for the PWQ based defect detection methods
described herein may use one or more characteristics of a region
proximate the defect (i.e., the "background" information) to classify
defects. For example, the method may include isolating the immediate
neighborhood of the background (which could be called a "micro-region")
and comparing the immediate neighborhood to others using standard
correlation and template matching algorithms, which may be any suitable
algorithms known in the art of image processing. The micro-region may be
defined by a 16×16 pixel image centered on the defect or containing
the defect. Alternatively, the micro-region may be defined by a
32×32 pixel image centered on the defect or containing the defect.
In some embodiments, the region proximate the defect may be a "greater
neighborhood" region of about 64 pixels×64 pixels.

[0084]In another example, instead of using acquired image data to define
the background proximate the defect, the defect location may be
determined in the GDS file of the design pattern (decorated or
un-decorated with RET features). A portion of the design pattern data in
the GDS file proximate the defect may be selected. The background in the
GDS file may be compared to other defective locations as is done with the
reference die images. The additional locations can then be correlated to
the defect location from the original inspection. The additional
locations may also be designated for review (e.g., by SEM).

[0085]In yet another example, the region proximate the defect may be
generated through aerial projection. In one such example, the aerial
image data may be taken from an aerial sensor such as that described in
co-owned U.S. patent application Ser. No. 10/679,857 filed Oct. 6, 2003
by Stokowski et al. now U.S. Pat. No. 7,123,356 issued Oct. 17, 2006,
which is incorporated by reference as if fully set forth herein.
Alternatively, the aerial image data could be generated by an aerial
image sensor of the type described in U.S. Pat. Nos. 6,803,554 to Ye et
al. and 6,807,503 to Ye et al. and U.S. Patent Application Publication
No. US 2003/0226951 by Ye et al., which is incorporated by reference as
if fully set forth herein, which are incorporated by reference as if
fully set forth herein.

[0086]By using the background features around and "behind" the defects,
the methods described herein are able to find the relevant changes in the
lithographic feature that can be lost in irrelevant defects using other
methods. As used herein, the term "background" refers to features of the
reference image that are immediately "behind" the defect image (i.e., the
features of the reference image or the design pattern data that are
located at the same pixel locations as the defect in the image of the
modulated die) and the region around the defect image (i.e., the features
of the image of the modulated die proximate the defect). In this manner,
by combining the results of "background binning" (e.g., grouping defects
on the basis of one or more characteristics of a region proximate the
defect) with PWQ type defect detection methods for ordering the results
of the repeating defect detection algorithm and prioritized die
information, the methods described herein are able to present the user
with information that can be used to find critical or relevant defects
faster than existing defect detection methods.

[0087]According to one embodiment, therefore, a computer-implemented
method for sorting defects in a design pattern of a reticle includes
searching for defects of interest in inspection data using priority
information associated with individual defects in combination with one or
more characteristics of a region proximate the individual defects. The
one or more characteristics of the region (i.e., the background
information) may be selected by a user. The inspection data is generated
by comparing images of the reticle generated for different values of a
lithographic variable. The images include at least one reference image
and at least one modulated image. In this manner, the method involves
searching a relatively large amount of defect information for defects of
interest using the priority information generated by PWQ type inspection
in combination with the background information. The priority information
corresponds to a modulation level associated with the individual defects.

[0088]The method also includes assigning one or more identifiers to the
defects of interest. In one embodiment, the one or more identifiers may
include an indicator identifying if the defects of interest are to be
sampled. In one such embodiment, assigning the identifier(s) may be
performed automatically based on the priority information and the one or
more characteristics of the region proximate the individual defects. In
another embodiment, the identifiers may include one or more defect
classifications. The classifications may distinguish defect types using
user defined names in some embodiments. Assigning identifier(s) to the
defects may be performed as further described herein.

[0089]In some embodiments, the method may include grouping the defects of
interest based on the priority information, the one or more
characteristics of the region proximate the individual defects, or a
combination thereof. In another embodiment, the method may include
grouping the defects of interest based on the one or more characteristics
of the region proximate the individual defects in combination with one or
more characteristics of the defects of interest. Grouping the defects in
these embodiments may be performed as further described herein.

[0090]In this manner, the methods described herein may be used to find a
relatively small number of defects of interest from a large amount of
candidate defects. The inputs to the method may include defect
priorities, defect attributes, correlation to critical points from DRC,
and defective and reference images. The candidate defects may be filtered
based on the defect priorities and/or attributes to reduce the number of
candidate defects that are searched. In some embodiments, the features of
the defects and the background may be obtained and compressed for search.
The outputs of the method may include defects of interest with class
codes and review sample flags or any other identifiers known in the art.
The defects that are not of interest may be excluded from the inspection
data such that the number of defect candidates that are searched is
reduced.

[0091]The embodiments of the method described above may include any other
steps described herein. For example, the method may include retrieving or
finding similar defects based on search criteria and given defect
examples. In addition, the method may include performing a number of
functions to prepare for defect review such as providing status and
feedback to a user, generating charts for defect population in terms of
groups, classes, and/or priorities, generating tags for defect priorities
and review samples, generating a defect list with defect information, and
generating folders for classified or excluded defects. In addition, the
method may include sampling defects for later processing, which may be
performed as described herein, and which may reduce the number of defect
samples that are reviewed or processed. Furthermore, as described further
herein, the methods may be customized by the user depending on, for
example, the defects of interest by changing criteria for filtering,
grouping, retrieving, classifying, sampling, manually overriding the
results of automated operations, and repeating any step(s) at any time.

[0092]For example, in one embodiment, if a defect is determined to be
present in the design pattern of a reticle, the computer-implemented
methods described herein may include assigning the defect to a group
based on one or more characteristics of a region proximate the defect.
Grouping can be performed by either supervised or unsupervised
classification techniques, which are known in the art of pattern,
recognition. The one or more characteristics of the region that are used
for assigning the defect to a group may include one or more
characteristics of the design pattern in the region. In addition, the one
or more characteristics of the region that are used for assigning the
defect to a group may include one or more characteristics of the region
in one or more images used for the comparison. In other words; the
characteristic(s) of the region may include the characteristic(s) of the
region in the modulated die in which the defect was detected in addition
to the characteristic(s) of the corresponding region in the one or more
reference dies that were compared with the modulated die. The
characteristic(s) of the region that are used for sorting the defects
into groups may also be selected by a user. The user may select the
characteristic(s) prior to grouping the defects as described further
herein.

[0093]In one embodiment, assigning the defects to a group may include
comparing an image of the region proximate the defect to images of the
regions that are proximate to other defects detected in the design
pattern. In another embodiment, a portion of a modulated die image
proximate the defect may be located in a GDS file image of the design
pattern. The portion of the GDS file image corresponding to the portion
of the modulated die image proximate the defect may be compared to other
similar locations in the modulated die image.

[0094]In addition, the portion of the GDS file image or other design
layout mapped to the defect may be used to determine one or more
characteristics of the region proximate the defect. These characteristics
may be determined using any other images or data such as a high
resolution image of the design pattern of the reticle. A high resolution
image of the design pattern may be obtained using any high resolution
reticle imaging system known in the art. In another embodiment, a
simulated aerial image of the design pattern of the reticle may be used
to determine one or more characteristics of the region proximate the
defect. The simulated aerial image may be generated using any suitable
simulation program known in the art. In a different embodiment, the one
or more characteristics of the region proximate the defect may be
determined from an aerial image of the reticle obtained using an aerial
imaging and measurement system (AIMS) as described further herein.

[0095]Examples of methods that may be used for classification of the
defects are illustrated in U.S. patent application Ser. No. 10/954,968 to
Huet et al. filed on Sep. 30, 2004, now U.S. Pat. No. 7,142,992 issued
Nov. 28, 2006, which is incorporated by reference as if fully set forth
herein. Examples of additional methods that may be used for sorting and
classifying defects are illustrated in U.S. Patent Application Ser. No.
60/618,475 to Teh et al. filed on Oct. 12, 2004, which is incorporated by
reference as if fully set forth herein.

[0096]After sorting the defects into groups, the method may include
assigning a defect classification to one or more defects or the entire
group. The same classification can be assigned to defects in different
groups. Classification of the different groups of defects may include
analyzing one or more characteristics of one or more defects in the
group. For example, the method may include analyzing one or more
characteristics of one or more defects in the group to determine if the
group of defects is an irrelevant defect group. The method may also
include analyzing one or more characteristics of one or more defects in
the group to determine if the group indicates a failure in the design
pattern. Classifying the different groups of defects may also or
alternatively include analyzing one or more characteristics of the
background features around and behind the defects.

[0097]The methods described herein may also include a number of other
filtering and/or sorting functions. For example, the method may include
comparing the defects of interest to inspection data generated by design
rule checking (DRC) performed on design pattern data of the reticle to
determine if the defects of interest correlate to DRC defects. In one
such embodiment, the method may include removing from the inspection data
the DRC defects that do not correlate with the defects of interest.

[0098]In such embodiments, the locations of the defect are correlated to
known vulnerable points based on the results of DRC. DRC can produce a
list of critical points (sometimes referred to as "hot spots"). These
points can be used alone, directly as a guide for inspection and/or
measurements of reticle design pattern. However, the DRC often produces
too many points for inspection and/or measurement. Therefore, the
critical points identified by the DRC can be filtered as described herein
using one or more characteristics of a region proximate the critical
points alone to reduce the population of the critical points. In
addition, or alternatively, the critical points may be filtered using the
"Defects Like Me" function described herein to reduce the population. In
this manner, inspecting, measuring, and/or reviewing critical points that
are similar may be reduced, and even eliminated.

[0099]In addition, the critical points identified by the DRC may be
overlaid with the inspection data generated as described herein. The
inspection data may be data generated by imaging a wafer on which one or
more modulated dies and one or more reference dies are printed.
Alternatively, the inspection data may include aerial images of the
reticle design pattern generated by simulation or experimentation. In
this manner, the defects of interest found as described herein may be
compared to inspection data generated by design rule checking to
determine if the defects of interest correlate to design rule checking
defects. The inspected defects that do not correlate with the DRC results
may then be removed from the inspection data. In each of the examples
provided above, ORC results may be used instead of DRC results.

[0100]The background binning methods described above have been shown to
group the defects effectively such that the relevant defects can be found
faster. In the case of PWQ methods, the background is sometimes the only
relevant feature group, and so during a PWQ experiment, the system can
use this feature set to group defects with similar backgrounds into the
same bins. These background features may be divided into a number of
different subgroups (e.g., three subgroups), which may be presented to
users on a graphical user interface (GUI) such as those described herein.
The different subgroups may include, for example, statistic measures of
image intensity, statistic measures of image intensity variation, and
measures of elementary image structures. Users can choose a combination
of background subgroups to use in the PWQ binning.

[0101]FIG. 6 is a screenshot illustrating one example of a user interface
that can be used to sort defects detected by the methods described
herein. In particular, a user will be able to choose which subgroups of
background to use for binning of the defects, and FIG. 6 illustrates one
possible user interface for selecting the subgroups. As shown in FIG. 6,
the user interface includes Defects Flow box 12, which includes a number
of options for the user. For example, Defects Flow box 12 includes
Filtering by Priorities section 14. In this section, the user may select
the defect priorities to use for filtering. The priorities may be
selected individually by clicking on the boxes next to the priority
numbers. Alternatively, the user may select all priorities or none of the
priorities by clicking one of the buttons below the listing of the
individual priorities.

[0102]The PWQ defects are prioritized by the modulation level (e.g., M1,
M2, M3, etc.) where they were first detected (as determined in the setup
of the experiment) and within modulation by the number of occurrences of
the defect found in all modulated dies through the repeater stacking of
all of the defects in the same modulation direction, positive or negative
from nominal. Such prioritization of the defects is further described in
U.S. Patent Application Publication No. US2004/0091142 to Peterson et
al., which is incorporated by reference as if fully set forth herein. In
the user interface, the user is able to filter by this priority or select
defects with certain priorities to work with in Filtering by Priorities
section 14. Defects that do not fall within the selected priorities may
be eliminated from the defect data.

[0103]In another embodiment, the defects may be filtered using one or more
rules. The one or more rules may be based on, for example, one or more
characteristics of the defects. In one embodiment, the user may create
the rules that are used to filter the defects. For example, as shown in
FIG. 6A, the user interface may include Filtering Rule box 13. The
Filtering Rule box allows the user to create a filtering rule in a number
of different ways. For example, the user may enter a rule definition in
Rule Definition 13a section of the Filtering Rule box. In addition, the
user may select one or more elements 13b in Build the Rule section 13c by
checking the box beside those elements that are to be used to filter
defects. Although a number of different elements are illustrated in FIG.
6a, it is to be understood that the elements that are displayed in the
Build the Rule section may vary depending on, for example, the defect
characteristics that are of interest.

[0104]Depending on the element that is selected, a number of different
operators may be displayed in Operator section 13d. The user may select
an operator to be used with the selected element. The user may select the
operator by clicking on an operator or in any other manner known in the
art. In addition, the user may enter a value in Value section 13e that is
to be used with the selected element and operator. The values that are
available for selection may vary depending on the element and the
operator that were previously selected. Once the user has selected an
element and operator, Histogram 13f may be displayed in the Filtering
Rule box. Histogram 13f may illustrate the number of defects for
different values of the element and operator. In this manner, the user
may be presented with information about the defects while building the
rule such that the user may tailor the rule to efficiently filter the
defects.

[0105]As further shown in FIG. 6, Defects Flow box 12 also includes
Grouping Rule section 16. The Grouping Rule section allows the user to
select which characteristics of the background and/or the defect are to
be used for grouping or sorting of the defects. For example, as shown in
Grouping Rule section 16, the user may select one or more background
features or Context Features 18 for grouping. As shown in FIG. 6, the
Context Features may include brightness, roughness, and pattern although
the Context Features that are available to the user may include any other
background features known in the art. In addition, although all three
Context Features are shown to be selected in FIG. 6, it is to be
understood that the user may select fewer than all of the available
Context Features, any combination of the available Context Features, or
none of the available Context Features.

[0106]The user may also or alternatively select one or more Defect
Features 20 to be used for grouping of the defects. As shown in FIG. 6,
the Defect Features may include size, shape, brightness, contrast, and
background. However, the Defect Features that are available to the user
may include fewer than all of these features. In addition, the Defect
Features that are available to the user may include any other appropriate
feature(s) of defects that may be used for grouping. As further shown in
FIG. 6, the user may select none of the Defect Features for grouping of
the defects. In particular, since the Defect Features may not necessarily
be useful for grouping of defects detected in the PWQ-type methods
described herein, for the methods described herein, the user may not
select any of the Defect Features. However, the user may alternatively
select one or more of the Defect Features to be used alone for grouping
of the defects or to be used in combination with the Context Features for
grouping.

[0107]The Grouping Rule Section also includes Number of Groups option 22.
The user may select or alter the number of groups into which the defects
are sorted using the Number of Groups option. In this example, the user
may type a number of groups into the box, click the arrows next to the
box until the selected number appears, or move the arrow along the scale
until the selected number appears in the box. The number of groups that
are selected will affect how finely the defect and/or context features
are divided among the groups. Therefore, a larger number of groups will
result in fewer and more similar defects assigned to each group. It is
noted that the number of groups may not be specified by the user.
Instead, the algorithm can automatically determine an appropriate number
of groups.

[0108]Defects Flow box 12 also includes Sampling section 24 as shown in
FIG. 6. The Sampling section includes Number of Defects for Review option
26, in which the user can select a total number of defects for review.
Review of the defects may be performed using any appropriate defect
review tool known in the art such as a SEM tool. The Sampling Section
also includes Defect Priorities to Sample option 28, in which the user
can select individual defect priorities that should be reviewed. As shown
in FIG. 6, the defect priorities may be selected for review individually.
However, the defect priorities may be selected in any manner known in the
art. In addition, the Sampling section includes Defect Classes to Sample
option 30, in which the user can select individual defect classes that
should be reviewed. The defect classes may be selected for review
individually as shown in FIG. 6 or in any other manner known in the art.
In addition, an automatic sampling algorithm may be used to select
defects for sampling using background and priority information. In some
embodiments, a list of sample sites to visit and/or measure during review
may be created based on the locations of the defects in the modulated
die(s). These locations may be correlated to the location within the
reticle such that the locations can be found automatically by the review
tool.

[0109]As further shown in FIG. 6, the Defects Flow box also includes a
number of buttons 32, which the user can select to apply the filtering,
grouping, and sampling operations to the defect data. The user can apply
these operations in any order. However, typically, a user may choose to
filter the defects before grouping them, and to group the defects before
sampling them for review. In this manner, the results of the background
binning will be combined with the results of prioritized filtering so
that the user can view sampled defects by a combination of the background
and priority. In addition, the filtering and grouping operations can be
performed iteratively rather than by fixed binning operations.

[0110]The remaining boxes shown in the screenshot of FIG. 6 can be used to
display the results of the filtering, grouping, and/or sampling
operations. However, these boxes may also be used to further work with
the defects. For example, the user interface shown in FIG. 6 includes
Defect Runs and Classes box 34, in which a stacked color bar chart is
illustrated showing the results of the filtering and grouping operations.
The stacked color bar chart can be used as a mechanism for illustrating
and working with defect groups and priority together. Each bar represents
a group of defects. Color indicates defect priorities. Although such a
chart may advantageously illustrate a substantial amount of information
about the defects in a relatively easy-to-comprehend manner, it is to be
understood that any method or graphical structure may be used to
illustrate the results of the filtering and/or grouping operations.

[0111]For example, the bar chart illustrated in FIG. 6 was displayed since
the Grouped Defects option 34a was selected. However, if the Filtered
Population and Defect Grid options are selected, different graphics will
be displayed. For example, FIG. 6B illustrates another manner in which
the results of filtering and grouping can be displayed when Defect Grid
option 34b is selected. As shown in FIG. 6B, the Defect Runs and Classes
box 34 may include grid 34c that illustrates the number of defects that
were found as a function of priority and group. Although a certain number
of priorities and groups are illustrated in FIG. 6B, obviously the number
of priorities and groups will vary depending on the parameters used for
filtering and grouping.

[0112]FIG. 6c illustrates a different manner in which the results of
filtering and grouping can be displayed when Filtered Population option
34d is selected. As shown in FIG. 6c, the number of defects that were
found as a function of priority are illustrated in bar chart 34e.
However, it is to be understood that the number of defects as a function
of priority may be illustrated in any other manner known in the art. In
addition, although three different priorities are illustrated in FIG. 6c,
it is to be understood that the number of priorities will vary depending
on the parameters used for filtering.

[0113]The user interface shown in FIG. 6 may also include Available
Defects box 36. The available defects box may illustrate verification
defects. For example, the available defects box may illustrate the
results from filtering, grouping, and retrieving. All defects which are
not classified or not in classified defects folders can be displayed in
an area. As shown in FIG. 6, the Available Defects box may illustrate
images of the defects. Alternatively, the Available Defects box may
provide information about the results using any suitable method known in
the art. In addition, the user may perform one or more functions on the
available defects using the Available Defects box.

[0114]The user interface may illustrate results of filtering and grouping
graphically as described above and with images of defects selected for
sampling. For example, as shown in Sample Defects box 38, the user
interface may illustrate a folder into which defects were classified. In
addition, if defects were assigned to the folders, representative defect
images may be illustrated on the front of the folders. The user may
perform a number of functions on the defect images shown in the Sample
Defects box. For example, the user may select one of the folders into
which defects were assigned. Selecting one of the folders may result in
illustration of the defect images in the selected folder in box 40 below
the illustration of the different folders. As shown in box 40, the defect
images may also be illustrated with a number. The number may indicate the
priority assigned to each defect image.

[0115]The user can move defects from one folder into another to change
defect classification. The user can also perform un-classification by
moving defects into the Available Defect Gallery. The user can add
folders, delete the folders and rename the folders. Deleting a folder
will un-classify all defects in that folder. The first folder, called
Defects-to-ignore, on the left is a folder for all defects that are
excluded from filtering, grouping and sampling. One or more such folders
can exist. Moving defects into classified folders can be achieved, but
not limited by, selecting followed by dragging and dropping the defects
into the folders, or by selecting followed by clicking a button, like the
Defects-to-ignore button. Although one manner of illustrating the sample
images to a user is illustrated in FIG. 6, it is to be understood that
any other manner of illustrating sample images may be used in the user
interface and methods described herein.

[0116]FIG. 6d illustrates another example of Sample Defects box 38. As
shown in Sample Defect box 38 of FIG. 6d, the folder for Class 2 of the
defects has been selected resulting in illustration of the defect images
in the Class 2 folder in box 40 below the illustration of the different
folders. Sampling can be performed either automatically or manually. By
clicking the Apply Sampling button of buttons 32 shown in FIG. 6, the
defects in classified folders are sampled according to the criteria set
for sampling. The user can also select one or more defects in classified
folders and mark them as sampled defects by clicking Mark Selected or
Mark All, as shown in FIG. 6d. All sampled defects may be tagged with a
marker. As further shown in FIG. 6d, the user can turn off the sample
status for one or more defects by using the button, Unmark Selected or
Unmark All.

[0117]The sample images may also be illustrated to the user in other
manners. For example, the user interface may be configured to display any
of the defects or just the sample images intermittently with reference
images corresponding to the defect images. In this manner, the images may
appear to flash in the user interface repeatedly one after the other.
Such "flashing" of the images may allow the user to gain additional
understanding of the differences between the images. In a similar manner,
sample images of differently modulated dies may be flashed in the user
interface, which may aid in user understanding of trends of the defects.

[0118]It is also noted that although the user interface is shown to
include four different boxes in FIG. 6, it is to be understood that the
user interface may include fewer than four information boxes or more than
four information boxes. In general, the amount and organization of the
information shown in the user interface may be designed to present the
maximum amount of information to a user in the most manageable and
easy-to-comprehend manner possible.

[0119]The user interfaces described herein provide a number of advantages
in comparison to other currently used user interfaces for processing
inspection results. Particularly, as described further above, the user
interface provides pre-filtering capability, which may be performed based
on priority and/or rules. The parameters of pre-filtering may be selected
by a user as described further above. In addition, the background
characteristic(s) that are used for grouping may also be selected as
described herein. The background characteristic(s) may also be used with
other defect attributes for grouping as described above. Furthermore, the
user interface can be used to perform iterative grouping rather than
fixed binning. An automatic sampling algorithm may also be used with the
background grouping and priority filtering results. The functionality of
the user interface may also be expanded, for example, to create a list of
sample sites to visit and/or measure based on the locations of modulated
die and then to make a "fake" result to be used.

[0120]As interesting defects are found, the user can also use a different
user interface to see other examples of defects that are similar to the
interesting defects using a defect retrieving feature called "Defects
Like Me." The user can also use this feature to remove groups of
irrelevant defects in order to quickly traverse through the large number
of defects. One example of a user interface that can be used to
illustrate defects that are similar is shown in FIG. 7. As shown in FIG.
7, the user interface includes Sample Defect box 42, which illustrates
the defect that is selected by the user. The user may select this defect
from the defects illustrated in another user interface such as that shown
in FIG. 6. As also shown in FIG. 7, the user may perform one or more
functions on the image of the selected defect using icons 44.

[0121]The user interface also includes Searching Criteria box 46, in which
the user can select one or more parameters for searching for defects that
are similar to the selected defect. In particular, Searching Criteria box
46 includes Manual Features Selection section 48. In the Manual Features
Selection section, the user can select one or more features of the
defects to use for searching for similar defects. As shown in FIG. 7, the
features that can be selected include size, brightness, shape, contrast,
polarity, and context. However, it is to be understood that the features
which are available for selection may include any appropriate features
known in the art.

[0122]As shown in Manual Features Selection section 48, the user may also
select all of the features or none of the features by clicking on the
appropriate button. Alternatively, the user may manually select
individual features by clicking on the boxes next to the feature name.
Although only the context feature is shown to be selected in FIG. 7, it
is to be understood that any of the other features may alternatively be
selected or a combination of features may be selected. As described
above, the context or background of the defects may be advantageously
used to group defects since the features of the defects themselves may
actually vary greatly from one modulated die to another. Therefore, the
selected context feature may frequently be used to search for similar
defects.

[0123]As further shown in FIG. 7, a weight may be assigned to each of the
features to be used for searching for similar defects. The weight
assigned to each feature may be a default weight assigned automatically
or upon selection of the appropriate button. Although each of the default
weights are shown to be the same, it is to be understood that the default
weights for individual features may vary. The user may assign different
weights to individual features in a number of different manners. For
example, the user may type a number for the weight into the box, click
the arrows next to the box until the selected weight appears, or move the
arrow along the scale until the selected weight appears in the box.

[0124]As shown in FIG. 7, Searching Criteria box 46 also includes
Sensitivity section 50, in which the user may select the sensitivity with
which defects are to be searched. The sensitivity may be selected in
different ways. For example, as shown in FIG. 7, the sensitivity may be
defined by the number of defects to which the search results are limited.
In other words, the number of defects shown in Sensitivity section 50 may
indicate to the computer-implemented method that the searching results
are to be limited to 50 defects (or some other number of defects) that
are most like the selected defect. Alternatively, the user may define the
sensitivity of the search by assigning a threshold to the features that
are selected for the search. Although only one threshold is shown in FIG.
7, it is to be understood that the number of threshold options that are
shown in FIG. 7 may vary depending on the number of features that are
selected for searching.

[0125]Searching Criteria box 46 also includes Start Searching button 52
which the user can click once the appropriate choices have been made in
the Searching Criteria box. During or after searching, images of the
defects that are determined to be like the selected defect based on the
searching criteria may be illustrated in Found Defects section 54 of the
user interface. As shown in FIG. 7, the user may perform a number of
different functions on the defect images using icons 56. In addition, the
user may select to accept the found defects using Accept button 58.
Alternatively, the user may decide to quit the "Defects Like Me" function
using Quit button 60.

[0126]It is noted that although the user interface is shown to include
three different boxes in FIG. 7, it is to be understood that the user
interface may include fewer than three information boxes or more than
three information boxes. In general, the amount and organization of the
information shown in the user interface may be designed to present the
maximum amount of information to a user in the most manageable and
easy-to-comprehend manner possible.

[0127]In additional embodiments, the methods described herein may include
altering the design pattern on the reticle based on the results of the
defect detection and/or sorting methods described herein. In particular,
the results of the methods described herein may be used to determine if
the reticle passes qualification standards for the reticle. If the
reticle does not pass qualification, then the reticle design pattern may
be altered. Preferably, the reticle design pattern is altered such that
fewer defects in the design pattern will be produced in the design
pattern printed on the wafer. A new reticle may then be fabricated with
the altered design pattern. Alternatively, in some instances, the reticle
may be physically altered to alter the design pattern on the reticle.
Physically altering the reticle may be performed using any repair process
known in the art such as focused ion beam repair processes.

[0128]In another embodiment, the methods described herein may include
generating a different design pattern for the reticle based on the
results of the defect detection and/or sorting methods described herein.
In particular, a new design pattern may be generated if the design
pattern that was inspected was found to have a substantially large amount
of defects, a relatively large number of defects that cannot be fixed,
and/or defects that cannot be fixed and will cause fatal flaws in the
design pattern that will be printed on the wafer. In yet another
embodiment, the results of the methods described herein may be fed
forward to the design process of other reticles. In particular, the
results of the methods described herein may be used to design RET
features in other reticles.

[0129]Some embodiments of the method may include determining a process
window of the reticle. For example, it may be determined if some smaller
range of the value of the lithographic variable that was examined can be
used to adequately reproduce the design pattern on wafers. In this
manner, the reticle may be qualified for use with a smaller than normal
process window. The degree to which the process window can be narrowed in
an acceptable manner will vary depending on, for example, the drift in
the lithographic variable that can be expected for lithography systems
that will use the reticle. In this manner, a defective reticle design
pattern may be used without fixing the defects in the reticle design
pattern.

[0130]Program instructions implementing methods such as those described
herein may be transmitted over or stored on the carrier medium. The
carrier medium may be a transmission medium such as a wire, cable, or
wireless transmission link, or a signal traveling along such a wire,
cable, or link. The carrier medium may also be a storage medium such as a
read-only memory, a random access memory, a magnetic or optical disk, or
a magnetic tape.

[0131]The program instructions may be implemented in any of various ways,
including procedure-based techniques, component-based techniques, and/or
object-oriented techniques, among others. For example, the program
instructions may be implemented using Matlab, Visual Basic, ActiveX
controls, C, C++ objects, C#, JavaBeans, Microsoft Foundation Classes
("MFC"), or other technologies or methodologies, as desired.

[0132]The processor may take various forms, including a personal computer
system, mainframe computer system, workstation, network appliance,
Internet appliance, personal digital assistant ("PDA"), television system
or other device. In general, the term "computer system" may be broadly
defined to encompass any device having one or more processors, which
executes instructions from a memory medium. In addition, the processor
may include a processor as described in the patent applications
incorporated by reference above, which are particularly suitable for
handling a relatively large amount of image data substantially
simultaneously.

[0133]FIG. 8 illustrates one embodiment of a system configured to perform
one or more of the computer-implemented methods described herein for
detecting and/or sorting defects. The system shown in FIG. 8 is
configured to inspect a wafer. Although the system is shown in FIG. 8 to
be an optical based imaging system, it is to be understood that the
system shown in FIG. 8 may be configured to image the wafer in a
different way. For example, the system may be configured to inspect a
wafer by imaging the wafer with electron beams (i.e., an electron beam
based imaging system or SEM).

[0134]The system includes processor 62. The processor may include any
suitable processor known in the art. For example, the processor may be an
image computer or a parallel processor. In addition, the processor may be
configured as described above. The system also includes carrier medium
64. The carrier medium may be configured as described above. For example,
carrier medium 64 includes program instructions 66, which are executable
on processor 62. The program instructions may be executable for
performing any of the embodiments of the methods described above. The
program instructions may be further configured as described above.

[0135]In some embodiments, the system may also include inspection and/or
review tool 68. Tool 68 may be configured to image wafer 70 and to
generate image data for the wafer that contains information about the
design pattern printed on the wafer by a reticle. Tool 68 may be coupled
to processor 62. For example, one or more components of tool 68 may be
coupled to processor 62 by a transmission medium (not shown). The
transmission medium may include "wired" and "wireless" portions. In
another example, detector 72 of tool 68 may be configured to generate
output 74. The output may be transmitted across a transmission medium
from detector 72 to processor 62. In some embodiments, the output may
also be transmitted through one or more electronic components coupled
between the detector and the processor. Therefore, output 74 is
transmitted from the tool to the processor, and program instructions 66
may be executable on the processor to detect and/or sort defects on the
wafer as described herein using the image data included in output 74.
Program instructions 66 may be further executable on the processor to
perform other functions described herein (e.g., "Defects Like Me"
searching, sorting defects by priority, selecting defects for sampling,
etc.).

[0136]Inspection and/or review tool 68 may be configured to generate
images of the wafer using any technique known in the art. In addition,
the tool includes stage 76 upon which wafer 70 may be disposed during
imaging or measurements. The stage may include any suitable mechanical or
robotic assembly known in the art. The tool also includes light source
78. Light source 78 may include any appropriate light source known in the
art. In addition, the tool may include beam splitter 80, which is
configured to direct light from light source 78 onto wafer 70 at angles
that are approximately normal to an upper surface of wafer 70. The beam
splitter may include any suitable beam splitter known in the art. The
tool further includes detector 72, which is configured to detect light
transmitted by beam splitter 80. The detector is also configured to
generate output 74. The detector may include any suitable detector known
in the art.

[0137]Although one general configuration of the inspection and/or review
tool is shown in FIG. 8, it is to be understood that the tool may have
any suitable configuration known in the art. For example, the tool may be
configured to perform a single channel imaging technique as shown in FIG.
8. Alternatively, the tool may be configured to perform a multiple
channel imaging technique. In addition, the optical tool may be replaced
with an e-beam inspection tool such as a CD SEM and the eS25 and eS30
systems, which are commercially available from KLA-Tencor. Such a tool
may be coupled to the processor as described above.

[0138]In another embodiment, the computer-implemented methods described
above may be performed using aerial images. For example, the methods
described herein may be implemented using an aerial image measurement
system (AIMS) technique, which may be better understood by reference to
FIG. 9. In FIG. 9, a system is shown having three detectors, i.e.,
detectors 101, 102 and 103. Each of these detectors may preferably be set
at a different focal position. For example, detector 101 could be at zero
defocus, detector 102 could be at +0.2 defocus, and detector 103 could be
at -0.2 defocus. Of course, these levels of defocus are only examples.
Any suitable range or levels of defocus could be used, and such levels
could be optimized empirically. It is not necessary to use a detector
having zero defocus, for example, and all of the detectors could be set
at varying levels of positive defocus, or at mixed levels of positive and
negative defocus.

[0139]Sample 104 is preferably a mask or reticle. As sample 104 is exposed
to illumination source 105, an aerial image is detected at the three
detectors. Because of their different focal positions, the aerial images
at each detector will have different levels of defocus. Images having
varying levels of defocus may be compared and analyzed using any of the
techniques previously set forth herein. In a preferred embodiment,
signals taken from a first detector, such as detector 101, are compared
to signals taken from a second detector, such as detector 102,
continuously as sample 104 is inspected. This is only one example, of
course, and images from any pairs of detectors could be compared.
Alternatively, comparisons could be made between detectors and
mathematical combinations of other detectors (such as a pixel by pixel
average between a pair of detectors, or a difference between another pair
of detectors). Preferably, the levels of defocus and/or the types of
comparisons between the signals from the various detectors (or
combinations thereof) are selected to provide the user with information
regarding RET defects and the appearance of such defects across a process
window.

[0140]In the embodiment shown in FIG. 9, it is possible to simultaneously
perform a conventional inspection and a process window qualification. The
purpose and methodology of the process window qualification (to find RET
defects and the like) has already been described herein. The purpose of
the conventional inspection is to find other types of defects, such as
defects resulting from reticle manufacturing errors and/or from
contaminants on the reticle. A method of such a conventional inspection
is described in U.S. Pat. No. 6,268,093 to Kenan et al., which is
incorporated by reference as if fully set forth herein. Other suitable
methods of performing such inspections are described in more detail in a
commonly assigned application by Stokowski et al. having U.S. Ser. No.
10/679,617, filed Oct. 6, 2003, now U.S. Pat. No. 7,379,175 issued May
27, 2008, which is incorporated by reference herein in its entirety and
for all purposes. Such suitable methods include, without limitation, a
die-to-database inspection in which the reticle is inspected by
comparison against a rendered database from which the reticle was
created.

[0141]In a preferred embodiment, the conventional inspection is done by
comparing signals from the same detector taken at nominally identical
portions of different dies. This inspection process works well for
multi-die reticles. The process window qualification is performed
substantially simultaneously, and may be achieved as already described
herein by comparing images at varying levels of defocus for each die. So
the conventional inspection might be achieved by comparing images from a
first die on sample 104 to images of a second die on sample 104, wherein
each image is detected using detector 101. At substantially the same time
as the images of each such die are collected for purposes of the
conventional inspection, for each such die an image from detector 101
and/or detector 102 or detector 103, is also compared to an image of that
same die taken at a different focal position (for example from another of
detectors 101, 102 and/or 103, or any mathematical combination thereon).
Thus, the conventional inspection and process window qualification may be
performed substantially simultaneously.

[0142]If desired, the processing of the data from the conventional
inspection and from the process window qualification could be performed
on the same computer by using parallel processing. A suitable
architecture and methodology are described in more detail in a commonly
assigned application by Goldberg et al. having U.S. Ser. No. 09/449,022,
filed Nov. 24, 1999, now U.S. Pat. No. 7,106,895 issued Sep. 12, 2006,
which is incorporated by reference herein in its entirety and for all
purposes.

[0143]In yet another embodiment of the invention, and in accordance with
the above description of the example shown in FIG. 9, a single die
reticle could be provided as sample 104, and only a process window
qualification may be performed using the apparatus shown in FIG. 9. Such
a technique may be desirable for all types of reticles, and may be
particularly desirable for single die reticles. This is because the
apparatus shown in FIG. 9 is in many ways inferior to other types of
inspection systems, such as the 3XX and 5XX series commercially available
from KLA-Tencor Corporation. Thus, it may be desirable to find
conventional defects using the KLA-Tencor tools, and then inspect the
same reticle again in an aerial image mode to locate RET defects by
varying the process window. As mentioned above, this may be particularly
desirable where sample 104 is a single die reticle. This avoids the need
to render the design database in a mode suitable for comparison against
the aerial image. Instead, the aerial image is used only for purposes of
finding RET defects, and the conventional inspection is done using a more
accurate tool which can directly compare the actual image of the reticle
to the rendered database (including the OPC features present therein).

[0144]Of course, if a suitably rendered database is available for
comparison against the AIMS image (rendered using the techniques
described, for example, in the application by Stokowski et al., as
mentioned above), a die-to-database inspection could be done using an
AIMS tool such as that shown in FIG. 9. In such a case, it is possible to
also do the inspection for RET defects by using a comparison against the
rendered database. For example, the conventional inspection could be
performed by comparing images from a detector at zero defocus to images
rendered from the database, also at zero defocus. The RET defects could
then be found by comparing the images from one or more detectors, at
varying levels of defocus, against the rendered database at zero defocus.
Or the database could also be, through simulation, rendered in a manner
that is consistent with a given level of defocus. In either situation,
the methods described herein could be applied to find RET defects.

[0145]The present invention is not limited to just finding RET defects by
varying the level of defocus. As noted above, varying sigma and/or the
numerical aperture (NA) of the system are also relevant to the process
window. Varying these parameters can, therefore, be used to find RET
defects. One method of achieving this is to take an image obtained using
an inspection under a first set of conditions (i.e., a first set of
sigma, NA and defocus), then take an image of the same reticle under a
second set of conditions (i.e, varying one or more of the NA, sigma and
defocus), and compare the resulting images. Such a method can be
implemented, using an apparatus such as that shown in FIG. 9, simply by
storing data taken from a first inspection of a reticle under a first set
of conditions, varying parameters such as sigma, NA and/or defocus on the
apparatus, and then re-inspecting the same reticle with the new parameter
settings in place. The images are aligned prior to comparison. The stored
data could be taken from inspection of an entire reticle (and stored on
an optical disk or other media having suitable storage space), or could
be taken across just a portion of the reticle (such as one or more
swaths). If only a portion of the reticle inspection data is stored,
storage might be appropriately handled in a memory buffer or the like. In
some embodiments, the stored data may represent a "reference reticle
field," or an aerial image of the reticle that would be produced at the
best known process conditions, which may be stored such that it can be
later used for transient repeating defect detection and/or non-transient
defect detection.

[0146]In another embodiment, stored data could be taken from inspection of
an entire die or just a portion of the die. In one such embodiment, the
die or the portion of the die may correspond to a design pattern that is
formed on the wafer using a reference value of a lithographic variable,
which in some embodiments may be the best known conditions. In this
manner, the stored data may represent a "reference die." In alternative
embodiments, the stored data may be a simulated image. For example, the
simulated image may be an image that would be printed on the wafer at the
reference member value. In one embodiment, the simulated image may be
generated from reticle design data. The reticle design data may be
altered based on the reference value to generate a simulated aerial image
of the reticle. In a different embodiment, the simulated image may be
generated from an aerial image of the reticle that is acquired by reticle
inspection. The simulated aerial image or the acquired aerial image may
be altered using a resist model to generate an image of the reticle that
would be printed on the wafer at the reference value.

[0147]The stored data may be compared to other die or portions of die on
the wafer to determine a presence of defects on the wafer. In some
embodiments, the die that are compared to the stored data may be printed
at different conditions (i.e., not the reference value). As such, the
stored data may be used to determine a presence of transient repeating
defects in the die or the portions of the die on the wafer.
Alternatively, the die that are compared to the stored data may be
printed at the same conditions as the stored data (i.e., the reference
value). Therefore, the stored data may be used to determine a presence of
non-transient defects in the die or the portions of the die on the wafer.

[0148]As shown in FIG. 9, the system may include a number of other
components including, but not limited to, homogenizer 106, aperture 107,
condenser lens 108, stage 109, objective lens 110, aperture 111, lens
112, beamsplitter 113, and processor or computer 114. The components may
be configured as described in more detail in a commonly assigned
application by Stokowski et al. having U.S. Ser. No. 10/679,617, filed
Oct. 6, 2003, now U.S. Pat. No. 7,379,175 issued May 27, 2008. These
components may be altered to provide varying parameters such as sigma,
NA, the type of illumination, and the shape of the beam. For example,
aperture 107 may be altered to change sigma, the NA, the type of
illumination, and the shape of the beam.

[0149]In a preferred embodiment, rather than directly comparing raw data
from each detector (and/or from a rendered database), it may desirable to
preprocess the data prior to comparison, as described in U.S. Patent
Application Publication No. US2004/0091142 to Peterson et al., which is
incorporated by reference as if fully set forth herein.

[0150]In another preferred embodiment, the data taken from inspection by
any method described herein (e.g., inspection using aerial images,
inspection of images printed on a wafer, inspection of simulated images
in accordance with DRC techniques, etc.) may be used to flag regions of a
reticle or wafer for review. The defects may be selected for review as
described above. The coordinates for such review could be stored by the
inspection apparatus and passed to a review tool (or performed on a
review tool integrated into the inspection apparatus). In one preferred
embodiment, the review tool is an aerial image review tool of the type
commercially available from Carl Zeiss, Inc., Germany. Potential RET
defect locations on a reticle are identified, and the coordinates are
passed to the Zeiss tool. Each such potential defect (or a sample
statistically selected from a group of such defects) is then reviewed at
varying levels of defocus (or other optical conditions, such as sigma or
NA) to further study the possible defect and its potential significance.

[0151]It is to be noted that the above methods that use aerial images may
also be performed in a similar manner using simulated images (e.g.,
images acquired using DRC techniques or ORC techniques).

[0152]Further modifications and alternative embodiments of various aspects
of the invention may be apparent to those skilled in the art in view of
this description. For example, computer-implemented methods for detecting
and/or sorting defects in a design pattern of a reticle are provided.
Accordingly, this description is to be construed as illustrative only and
is for the purpose of teaching those skilled in the art the general
manner of carrying out the invention. It is to be understood that the
forms of the invention shown and described herein are to be taken as the
presently preferred embodiments. Elements and materials may be
substituted for those illustrated and described herein, parts and
processes may be reversed, and certain features of the invention may be
utilized independently, all as would be apparent to one skilled in the
art after having the benefit of this description of the invention.
Changes may be made in the elements described herein without departing
from the spirit and scope of the invention as described in the following
claims.